|Title||From GWAS Peak to Causal Mutation; Utilizing p(ig)CADD Scores to Prioritize Sequence Variation|
|Author(s)||Derks, Martijn; Gross, Christian; Lopes, M.S.; Reinders, M.J.T.; Bosse, Mirte; Gjuvsland, Arne B.; Megens, Hendrik-Jan; Ridder, Dick de; Groenen, Martien|
|Event||Plant and Animal Genome XXVIII, San Diego, 2020-01-11/2020-01-15|
Animal Breeding and Genomics
Aquaculture and Fisheries
|Publication type||Unpublished lecture|
|Abstract||The genotype-phenotype link is a major research topic in life sciences, but remains highly complex to disentangle. Part of the complexity arises from the polygenicity of phenotypes, in which many (interacting) genes contribute to the observed phenotype. Genome wide association studies have been instrumental to associate genomic markers to important phenotypes. However, despite the vast increase of molecular data (e.g. whole genome sequences), pinpointing the causal variant underlying a phenotype of interest is still a major challenge, especially due to high levels of linkage disequilibrium.
In this study we present a method to prioritize genomic variation underlying traits of interest from genome wide association studies in pigs. First, we select all sequence variants associated with the trait. Subsequently, we prioritize variation by utilizing and integrating predicted variant impact scores, gene expression data, epigenetic marks for promotor and enhancer identification, and associated phenotypes in other (well-studied) mammalian species. The power of the method heavily relies on variant impact scores, for which we used pCADD, a tool which can assign scores to any
variant in the genome including those in non-coding regions. Using our methodology, we are able to either pinpoint the likely causal mutation or substantially narrow down the list of potential causal candidates from any association result. We demonstrate the efficacy of the tool by reporting known and novel causal variants, of which many affect (non-coding) regulatory sequences associated with important phenotypes in pigs.
This study provides a framework to pinpoint likely causal variation and genes underlying important phenotypes in pigs. Hence, the tool accelerates the discovery of new causal variants that could be directly implemented to improve selection. Finally, we report several common pathways and molecular mechanisms involved in analogous phenotypes between human and pig, proving the suitability of pig as a model to study human (metabolic) disease.